The cloud workflow doesn't come with a scheduling feature. Apart from that, what are all the differences between these two services in terms of features? In which use case should we prefer the workflow over composer or vice versa?
There are some key differences to consider when choosing between the two solutions :
A Composer instance needs to be in a running state to trigger DAGs and you'll also need to size your Cloud Composer instance based on your usage, You do not need to do this in Cloud Workflows as it is a Serverless service and you pay for anytime a workflow is triggered
Another key difference is that Cloud Composer is really convenient for writing and orchestrating data pipelines because of it's internal scheduler and also because of the provided Operators, You can interact with any Data services inside of GCP.
However, Cloud Workflows interacts with Cloud Functions, wich is a task that Composer cannot do really well.
Both Composer and Workflows support orchestrating multiple services and can handle long running workflows. Despite there being some overlap in the capabilities of these products, each has differentiators that make them well suited to particular use cases.
Composer is most commonly used for orchestrating the transformation of data as part of ELT or data engineering. Workflows, in contrast, is focused on the orchestration of HTTP-based services built with Cloud Functions, Cloud Run, or external APIs.
Composer is designed for orchestrating batch workloads that can handle a delay of a few seconds between task executions. It wouldn’t be suitable if low latency was required in between tasks, whereas Workflows is designed for latency sensitive use cases.
While you don’t have to worry about maintaining Airflow deployments in Composer, you do need to specify how many workers you need for a given Composer environment. Workflows is completely serverless; there is no infrastructure to manage or scale.
For further information refer to this google blog article and this one.
I am trying to build an app where the user is able to upload a file to cloud storage. This would then trigger a model training process (and predicting later on). Initially I though I could do this with cloud functions/pubsub and cloudml, but it seems that cloud functions are not able to trigger gsutil commands which is needed for cloudml.
Is my only option to enable cloud-composer and attach GPUs to a kubernetes node and create a cloud function that triggers a dag to boot up a pod on the node with GPUs and mounting the bucket with the data? Seems a bit excessive but I can't think of another way currently.
You're correct. As for now, there's no possibility to execute gsutil command from a Google Cloud Function:
Cloud Functions can be written in Node.js, Python, Go, and Java, and are executed in language-specific runtimes.
I really like your second approach with triggering the DAG.
Another idea that comes to my mind is to interact with GCP Virtual Machines within Cloud Composer through the Python operator by using the Compute Engine Pyhton API. You can find more information in automating infrastructure and taking a deep technical dive into the core features of Cloud Composer here.
Another solution that you can think of is Kubeflow, which aims to make running ML workloads on Kubernetes. Kubeflow adds some resources to your cluster to assist with a variety of tasks, including training and serving models and running Jupyter Notebooks. Please, have a look on Codelabs tutorial.
I hope you find the above pieces of information useful.
I am selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. I want to minimize service costs. I also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should I do?
A. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
B. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
D. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs. Configure the job to use non-default Compute Engine machine types when needed.
C!
Use Dataflow on pubsub to transform your data and let it write rows into BQ. You can monitor the ETL pipeline straight from data flow and use stackdriver on top. Stackdriver can also be used to start events etc.
Use autoscaling to minimize the number of manual actions. Basically when this solution is setup correctly, it doesn't need work at all.
I want to build some neural network models for NLP and recommendation applications. The framework I want to use is TensorFlow. I plan to train these models and make predictions on Amazon web services. The application will be most likely distributed computing.
I am wondering what are the pros and cons of SageMaker and EMR for TensorFlow applications?
They both have TensorFlow integrated.
In general terms, they serve different purposes.
EMR is when you need to process massive amounts of data and heavily rely on Spark, Hadoop, and MapReduce (EMR = Elastic MapReduce). Essentially, if your data is in large enough volume to make use of the efficiencies of Spark, Hadoop, Hive, HDFS, HBase and Pig stack then go with EMR.
EMR Pros:
Generally, low cost compared to EC2 instances
As the name suggests Elastic meaning you can provision what you need when you need it
Hive, Pig, and HBase out of the box
EMR Cons:
You need a very specific use case to truly benefit from all the offerings in EMR. Most don't take advantage of its entire offering
SageMaker is an attempt to make Machine Learning easier and distributed. The marketplace provides out of the box algos and models for quick use. It's a great service if you conform to the workflows it enforces. Meaning creating training jobs, deploying inference endpoints
SageMaker Pros:
Easy to get up and running with Notebooks
Rich marketplace to quickly try existing models
Many different example notebooks for popular algorithms
Predefined kernels that minimize configuration
Easy to deploy models
Allows you to distribute inference compute by deploying endpoints
SageMaker Cons:
Expensive!
Enforces a certain workflow making it hard to be fully custom
Expensive!
From AWS documentation:
Amazon EMR is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark, on AWS to process and analyze vast amounts of data. By using these frameworks and related open-source projects, such as Apache Hive and Apache Pig, you can process data for analytics purposes and business intelligence workloads. Additionally, you can use Amazon EMR to transform and move large amounts of data into and out of other AWS data stores and databases, such as Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.
(...) Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
Conclussion:
If you want to deploy AI models just use AWS SageMaker
I am using Google Data Flow to implement an ETL data ware house solution.
Looking into google cloud offering, it seems DataProc can also do the same thing.
It also seems DataProc is little bit cheaper than DataFlow.
Does anybody know the pros / cons of DataFlow over DataProc
Why does google offer both?
Yes, Cloud Dataflow and Cloud Dataproc can both be used to implement ETL data warehousing solutions.
An overview of why each of these products exist can be found in the Google Cloud Platform Big Data Solutions Articles
Quick takeaways:
Cloud Dataproc provides you with a Hadoop cluster, on GCP, and access to Hadoop-ecosystem tools (e.g. Apache Pig, Hive, and Spark); this has strong appeal if you are already familiar with Hadoop tools and have Hadoop jobs
Cloud Dataflow provides you with a place to run Apache Beam based jobs, on GCP, and you do not need to address common aspects of running jobs on a cluster (e.g. Balancing work, or Scaling the number of workers for a job; by default, this is automatically managed for you, and applies to both batch and streaming) -- this can be very time consuming on other systems
Apache Beam is an important consideration; Beam jobs are intended to be portable across "runners," which include Cloud Dataflow, and enable you to focus on your logical computation, rather than how a "runner" works -- In comparison, when authoring a Spark job, your code is bound to the runner, Spark, and how that runner works
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
This flowchart from the google website explains how to go about choosing one over the other.
https://cloud.google.com/dataflow/images/flow-vs-proc-flowchart.svg
Further details are available in the below link
https://cloud.google.com/dataproc/#fast--scalable-data-processing
Same reason as why Dataproc offers both Hadoop and Spark: sometimes one programming model is the best fit for the job, sometimes the other. Likewise, in some cases the best fit for the job is the Apache Beam programming model, offered by Dataflow.
In many cases, a big consideration is that one already has a codebase written against a particular framework, and one just wants to deploy it on the Google Cloud, so even if, say, the Beam programming model is superior to Hadoop, someone with a lot of Hadoop code might still choose Dataproc for the time being, rather than rewriting their code on Beam to run on Dataflow.
The differences between Spark and Beam programming models are quite large, and there are a lot of use cases where each one has a big advantage over the other. See https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison .
Cloud Dataflow is a serverless data processing service that runs jobs written using the Apache Beam libraries. When you run a job on Cloud Dataflow, it spins up a cluster of virtual machines, distributes the tasks in your job to the VMs, and dynamically scales the cluster based on how the job is performing. It may even change the order of operations in your processing pipeline to optimize your job.
So use cases are ETL (extract, transfer, load) job between various data sources / data bases. For example load big files from Cloud Storage into BigQuery.
Streaming works based on subscription to PubSub topic, so you can listen to real time events (for example from some IoT devices) and then further process.
Interesting concrete use case of Dataflow is Dataprep. Dataprep is cloud tool on GCP used for exploring, cleaning, wrangling (large) datasets. When you define actions you want to do with your data (like formatting, joining etc), job is run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values.
Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.
Super fast — Without using Dataproc, it can take from five to 30
minutes to create Spark and Hadoop clusters on-premises or through
IaaS providers. By comparison, Dataproc clusters are quick to start,
scale, and shutdown, with each of these operations taking 90 seconds
or less, on average. This means you can spend less time waiting for
clusters and more hands-on time working with your data.
Integrated — Dataproc has built-in integration with other Google
Cloud Platform services, such as BigQuery, Cloud Storage, Cloud
Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than
just a Spark or Hadoop cluster—you have a complete data platform.
For example, you can use Dataproc to effortlessly ETL terabytes of
raw log data directly into BigQuery for business reporting.
Managed — Use Spark and Hadoop clusters without the assistance of an
administrator or special software. You can easily interact with
clusters and Spark or Hadoop jobs through the Google Cloud Console,
the Cloud SDK, or the Dataproc REST API. When you're done with a
cluster, you can simply turn it off, so you don’t spend money on an
idle cluster. You won’t need to worry about losing data, because
Dataproc is integrated with Cloud Storage, BigQuery, and Cloud
Bigtable.
Simple and familiar — You don’t need to learn new tools or APIs to
use Dataproc, making it easy to move existing projects into Dataproc
without redevelopment. Spark, Hadoop, Pig, and Hive are frequently
updated, so you can be productive faster.
If you want to migrate from your existing Hadoop/Spark cluster to the cloud, or take advantage of so many well-trained Hadoop/Spark engineers out there in the market, choose Cloud Dataproc; if you trust Google's expertise in large scale data processing and take their latest improvements for free, choose DataFlow.
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
Cloud Dataproc and Cloud Dataflow can both be used for data processing, and there’s overlap in their batch and streaming capabilities. You can decide which product is a better fit for your environment.
Cloud Dataproc is good for environments dependent on specific Apache big data components:
- Tools/packages
- Pipelines
- Skill sets of existing resources
Cloud Dataflow is typically the preferred option for green field environments:
- Less operational overhead
- Unified approach to development of batch or streaming pipelines
- Uses Apache Beam
- Supports pipeline portability across Cloud Dataflow, Apache Spark, and Apache Flink as runtimes.
See more details here https://cloud.google.com/dataproc/
Pricing comparision:
DataProc
Dataflow
If you want to calculate and compare cost of more GCP resources, please refer this url https://cloud.google.com/products/calculator/
One of the other important difference is:
Cloud Dataproc:
Data mining and analysis in datasets of known size
Cloud Dataflow:
Manage datasets of unpredictable size
see
Cloud Dataflow
Is a serverless data processing service that runs jobs written using
the Apache Beam libraries.
When you run a job on Cloud Dataflow it gets operated like this:
It spins up a cluster of virtual machines
Distributes the tasks in your job to the VMs, and dynamically scale the cluster based on how the job is performing
Dataflow may even change the order of operations in your processing pipeline to optimize your job.
It supports both batch and streaming Jobs. So use cases are ETL (extract, transfer, load) jobs between various data sources/databases.
For example, load big files from Cloud Storage into Big Query.
Streaming works based on subscription to Pub-Sub topic, so you can listen to real-time events (for example from some IoT devices) and then further process the data.
An interesting concrete use case of Dataflow is Data prep.
Data prep is a cloud tool on GCP used for exploring, cleaning, and wrangling (large) datasets. When you define the actions you want to perform on your data (like formatting, joining etc.), the job run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates" which can help simplify common tasks where the differences are parameter values.
Data proc
Is a managed Spark and Hadoop service that lets you take advantage of
open-source data tools for batch processing, querying, streaming, and
machine learning.
Data proc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.